Novel methods and devices to deliver substitution therapy in addiction

提供成瘾替代疗法的新方法和设备

基本信息

批准号：
10325846
负责人：
Michael Easton
金额：
$ 25.39万
依托单位：
ATMAN THERAPEUTICS CO.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-10 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10325846
关键词：
Abstinence Address Adherence Affect Anxiety Austria Buprenorphine COVID-19 pandemic Classification Clinical Clinical Trials Complex Computer software Data Data Set Data Sources Deglutition Detection Development Devices Dose Drug Prescriptions Engineering Ensure Equipment Event Face Family Feedback Fluorescence Funding Geography Goals Guidelines Health Benefit Health system Heroin Home Housing Human Ingestion Intake Intelligence Intervention Intuition Investigation Italy Legal patent Life Life Style Liquid substance Machine Learning Measures Meta-Analysis Methadone Methods Naltrexone Outcome Patients Persons Pharmaceutical Preparations Phase Population Preparation Provider Public Health Research Research Project Grants Resistance Risk Sales Sampling Savings Secure Security Services Severities Small Business Innovation Research Grant Social support Sodium Oxybate Specificity Stress Substance Use Disorder Support Groups System Technology Therapeutic Tobacco Use Disorder Training Unemployment United States Weight addiction alcohol abuse therapy alcohol effect alcohol use disorder algorithm training base contingency management convolutional neural network cost craving deep learning algorithm design drug of abuse effective therapy evidence base innovation light weight machine learning algorithm machine learning method medication compliance meter methadone treatment microphone mobile application neurobehavioral disorder novel novel therapeutics opioid use disorder patient oriented portability pressure pressure sensor prevent product development prototype psychosocial sensor sensor technology social engagement socioeconomics standard of care treatment adherence

项目摘要

PROJECT SUMMARY Current treatment options for addiction do not sufficiently address the clinical needs of patients. Due to low efficacy, misaligned treatment objectives, or lack of accessibility, patients are not able to durably abstain from substances or reduce their use to safer levels. Substitution therapy (ST) is a proven treatment approach and has resulted in massive global public health benefits for tobacco use disorder and opioid use disorder; however, the potential impact of ST is limited by concerns for causing more widespread harms. Thus substitution therapy is either underutilized or underexplored in other addictions due to risks of abuse, misuse, and diversion. Through implementing safe prescribing systems, health systems have solved for the provision of existing ST like methadone and buprenorphine. Nevertheless, there remains significant potential for broader access to current and novel ST treatments by utilizing advancement in software and hardware technology. Atman Therapeutics is an early-stage company seeking to develop novel therapeutics in addictive disorders. We are developing a platform to enable increased access to as well as novel development of ST. To achieve this objective, our platform uses cutting-edge machine learning methods, patient-centered research, and sophisticated hardware technology. Our technology will also enable the concurrent delivery of contingency management services, thus creating safe, engaging, and durably effective treatments for these complex neurobehavioral disorders. This Small Business Innovation and Research (SBIR) project aims to establish the feasibility of machine learning and integrated sensor technologies to confirm medication adherence in a diverse sample of patients. We will develop a deep-learning algorithm utilizing a feed-forward convolutional neural network (CNN), such as ResNet- 50, U-NET, PCU-Net, to enact classification of medication ingestion events. We will also demonstrate the feasibility of integrating a hardware microcontroller unit with multi-dimensional MEMS sensors into a device that can classify ingestion events in a highly sensitive and specific manner. These efforts will demonstrate the early feasibility of the Atman platform and form the basis for regulated development efforts of ST-enabling products and services.

项目概要目前的成瘾治疗方案并不能充分满足患者的临床需求。由于低疗效、治疗目标不一致或缺乏可及性，患者无法持久戒除物质或将其使用减少到更安全的水平。替代疗法 (ST) 是一种行之有效的治疗方法，为烟草使用障碍和阿片类药物使用障碍带来了巨大的全球公共卫生益处；然而，由于担心造成更广泛的危害，科学技术的潜在影响受到限制。因此替代疗法是由于滥用、误用和转移的风险，在其他成瘾行为中没有得到充分利用或探索不足。通过通过实施安全处方系统，卫生系统已经解决了提供现有 ST 的问题，例如美沙酮和丁丙诺啡。尽管如此，更广泛地获取当前信息仍然存在巨大潜力。利用软件和硬件技术的进步进行新颖的 ST 治疗。 Atman Therapeutics 是一家早期公司，致力于开发成瘾性疾病的新型疗法。我们正在开发一个平台，以增加对 ST 的访问和新颖的开发。为了实现这一目标目标，我们的平台使用尖端的机器学习方法、以患者为中心的研究，以及先进的硬件技术。我们的技术还可以同时交付应急措施管理服务，从而为这些复杂的疾病创造安全、有吸引力且持久有效的治疗方法神经行为障碍。该小型企业创新与研究 (SBIR) 项目旨在建立机器学习的可行性以及集成传感器技术，以确认不同患者样本的药物依从性。我们将利用前馈卷积神经网络 (CNN) 开发深度学习算法，例如 ResNet- 50、U-NET、PCU-Net，制定药物摄入事件的分类。我们还将展示将硬件微控制器单元与多维 MEMS 传感器集成到设备中的可行性可以以高度敏感和特定的方式对摄入事件进行分类。这些努力将证明早期 Atman 平台的可行性，并为 ST 支持产品的规范开发工作奠定基础和服务。